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Title: Big Data and Deep Learning in the Defense Acquisition Visibility Environment (DAVE)
Authors: Ying Zhao
Keywords: Lexical Link Analysis
Big Data
Big Acquisition Data
Big Data Architecture
Big Data Analytics
Quantum Computing
Quantum Mechanics
Quantum Game Theory and Quantum Intelligence.
Issue Date: 7-Dec-2016
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Big Data
Abstract: I have been studying Department of Defense (DoD) acquisition decision-making since 2009. The U.S. DoD acquisition process is extremely complex. There are three key processes that must work in concert to deliver capabilities: determining warfighters requirements and needs, planning the DoD budget, and procuring final products. Each process produces large amounts of information (Big Data). There is a critical need for automation, validation, and discovery to help acquisition professionals, decision-makers, and researchers understand the important content within large data sets and optimize DoD resources throughout the processes. I have been applying Lexical Link Analysis (LLA), Collaborative Learning Agents (CLA), and System Self-Awareness (SSA) to reveal and depict to decision-makers the correlations, associations, and program gaps across all acquisition programs (including their subsets) examined over many years. This enables strategic understanding of data gaps and potential trends, and can inform managers about the areas that might be exposed to higher program risk, and about how resource and big data management might affect the desired return on investment (ROI) for projects. In last year's research, I extended LLA/CLA/SSA in the context of quantum games and quantum intelligence, which can help the systems of systems, such as DoD acquisition systems, reach Nash Equilibrium and at the same time be Pareto optimal. This theory is capable of making the competitive systems cooperate in terms of systems of systems, such as DoD acquisition systems, for example, the theory can be applied to the current acquisition research to select systems of systems by balancing the expertise attributes (i.e., the system attributes that help to reach Nash Equilibrium) and authoritative attributes (i.e., the system attributes that help to reach Pareto optimal). I also co-supervised a Naval Postgraduate School (NPS) thesis using LLA and data from the Defense Acquisition Visibility Environment (DAVE) and discovered the characteristics of the success of software-intensive acquisition systems.
Description: Acquisition Management / NPS Faculty Research
Appears in Collections:Sponsored Acquisition Research & Technical Reports

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